ML Anthology

Leveraging Cognitive Context for Object Recognition

Wallace E. Lawson, Laura M. Hiatt, J. Gregory Trafton
CVPRW 2014 pp. 387-392
doi:10.1109/CVPRW.2014.63 /cvprw/2014/lawson2014cvprw-leveraging/

Abstract

Contextual information can greatly improve both the speed and accuracy of object recognition. Context is most often viewed as a static concept, learned from large image databases. We build upon this concept by exploring cognitive context, demonstrating how rich dynamic context provided by computational cognitive models can improve object recognition. We demonstrate the use cognitive context to improve recognition using a small database of objects.

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Cite

Text

Lawson et al. "Leveraging Cognitive Context for Object Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014. doi:10.1109/CVPRW.2014.63

Markdown

[Lawson et al. "Leveraging Cognitive Context for Object Recognition." IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, 2014.](https://mlanthology.org/cvprw/2014/lawson2014cvprw-leveraging/) doi:10.1109/CVPRW.2014.63

BibTeX

@inproceedings{lawson2014cvprw-leveraging,
  title     = {{Leveraging Cognitive Context for Object Recognition}},
  author    = {Lawson, Wallace E. and Hiatt, Laura M. and Trafton, J. Gregory},
  booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops},
  year      = {2014},
  pages     = {387-392},
  doi       = {10.1109/CVPRW.2014.63},
  url       = {https://mlanthology.org/cvprw/2014/lawson2014cvprw-leveraging/}
}